Determining hardware requirements for a wireless network event using crowdsourcing

ABSTRACT

Systems and methods for determining hardware requirements for a wireless network event are disclosed. In embodiments, a method comprises obtaining, by a computing device, social user data over a period of time from a plurality of mobile devices associated with a social event at a location; obtaining, by the computing device, bandwidth usage data for each of the plurality of mobile devices based on the social user data; obtaining, by the computing device, crowd density and traffic pattern data related to the social event; determining, by the computing device, participant movement data for the social event based on the crowd density and traffic pattern data; deriving, by the computing device, a social bandwidth density model based on the bandwidth usage data and the participant movement data; and determining, by the computing device, bandwidth requirements within a geo-spatial boundary associated with the social event from the social bandwidth density model.

BACKGROUND

The present invention relates generally to wireless network systems and,more particularly, to determining hardware requirements for a wirelessnetwork event using crowdsourcing.

Different generations of wireless technology are defined by their datatransmission needs. Like other cellular networks, fifth generation or 5Gwireless technology utilizes a system of cell sites (e.g., utilizingmacro cells or small cells) that divide a territory into sectors, andsends encoded data through radio waves. In general, macro cells or macrocell antennas are used in cellular networks with the function ofproviding radio coverage to a large area of mobile network access. Macrocell antennas (e.g., master base stations) are generally mounted onground-based masts, rooftops, or existing structures having a clear viewof the surroundings. In general, small cells or small cell antennascomprise low-powered cellular radio access nodes that are “small”compared to macro cells, and may be in the form of a portable or mobileantenna array or a fixed antenna array.

SUMMARY

In an aspect of the invention, a computer-implemented method includes:obtaining, by a computing device, social user data over a period of timefrom a plurality of mobile devices associated with a social event at alocation; obtaining, by the computing device, bandwidth usage data foreach of the plurality of mobile devices based on the social user data;obtaining, by the computing device, crowd density and traffic patterndata related to the social event; determining, by the computing device,participant movement data for the social event based on the crowddensity and traffic pattern data; deriving, by the computing device, asocial bandwidth density model based on the bandwidth usage data and theparticipant movement data; and determining, by the computing device,bandwidth requirements within a geo-spatial boundary associated with thesocial event from the social bandwidth density model.

In another aspect of the invention, there is a computer program productcomprising a computer readable storage medium having programinstructions embodied therewith. The program instructions are executableby a computing device to cause the computing device to: obtain crowdsourced social user data over a period of time from a plurality ofmobile devices associated with a social event at a location, wherein thesocial user data comprises data regarding the use of digital content foreach of the plurality of mobile devices; obtain bandwidth usage dataincluding an amount of bandwidth utilized by each of the plurality ofmobile devices during the use of digital content; generate participantmovement data for the social event indicating the movement ofparticipants at the location; derive a social bandwidth density modelbased on the bandwidth usage data and the participant movement data; anddetermine bandwidth requirements within a geo-spatial boundaryassociated with the social event from the social bandwidth densitymodel.

In another aspect of the invention, there is a system including: aprocessor, a computer readable memory and a computer readable storagemedium associated with a computing device; program instructions toobtain crowd sourced social user data over a period of time from aplurality of mobile devices associated with a social event at alocation, wherein the social user data indicates a manner in which eachof the plurality of mobile devices is utilizing bandwidth of a mobilenetwork; program instructions to obtain bandwidth usage data indicatingthe amount of bandwidth utilized by each of the plurality of mobiledevices based on the social user data; program instructions to obtaincrowd density and traffic pattern data related to the social event;program instructions to determine participant movement data for thesocial event based on the crowd density and traffic pattern data;program instructions to derive a social bandwidth density model based onthe bandwidth usage data and the participant movement data; and programinstructions to determine bandwidth requirements within a geo-spatialboundary associated with the social event from the social bandwidthdensity model, wherein the program instructions are stored on thecomputer readable storage medium for execution by the processor via thecomputer readable memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 depicts a computing infrastructure according to an embodiment ofthe present invention.

FIG. 2 shows an exemplary environment in accordance with aspects of theinvention.

FIG. 3 shows a flowchart of steps of a method in accordance with aspectsof the invention.

FIG. 4A shows a temporal plot illustrating clusters of high, medium andlow bandwidth usage at an event, generated in accordance withembodiments of the invention.

FIG. 4B shows the plot of FIG. 4A, including a Double Gaussian curve fitto user movement data and bandwidth (QoE) requirements of an event,generated in accordance with embodiments of the invention.

FIGS. 5A-5D illustrate fitted curves computed at regular intervals inaccordance with embodiments of the invention.

FIG. 6 illustrates optimal locations of network antennas along thefitted curve of FIG. 4B, generated in accordance with embodiment of theinvention.

FIG. 7 illustrates an exemplary use scenario in accordance withembodiments of the invention.

DETAILED DESCRIPTION

The present invention relates generally to wireless network systems and,more particularly, to determining hardware requirements for a wirelessnetwork event using crowdsourcing. In embodiments, social userinformation (e.g., mobile device usage information) is mined by a serverto understand the types of activities a user engages in at an event(e.g., outdoor social event). The social footprint of each participantat the event is utilized by the server to understand the types ofservices and network usage each participant consumers over time. Crowddensity and traffic pattern data is mined by the server to understandthe movement of participants within a crowd during the event. Using themovement information combined with bandwidth usage data, the serverderives a social bandwidth density model (SBDM) as a function of thephysical location of the event. The SBDM model is then used to inferQuality of Experience (QoE) requirements within a geo-spatial boundaryassociated with the event. In general QoE is a user-centric conceptconcerning the process of human perception and experience, and ofquality formation. Parameters effecting QoE may include latency, jitterand wireless signal strength or bandwidth, for example. QoE is dependenton the location of a user (consumer of wireless services). With QoErequirements known, small mobile cell systems (e.g., radio antennas) canbe deployed within a known user trajectory (path followed byparticipant) at the event to meet the needs of participants.

Wireless network carriers face a number of technical challenges whenproviding developing 5G wireless network access to users. Three corechallenges include: inter-cell interference, efficient medium accesscontrol, and traffic management. In particular, variations in size oftraditional macro cells and concurrent small cells may lead tointer-cell interference. Additionally, in situations where densedeployment of network access points and user terminals are required,user throughput will be low, latency will be high, and hotspots will notbe competent to cellular technology to provide high throughput. In suchsituations, efficient control of medium access is required. Further, incomparison to traditional human to human traffic in cellular networks, agreat number of machine to machine (M2M) devices in a cell may causeserious system challenges (i.e., radio access network (RAN) challenges),which may cause overload and congestion of a wireless network system.With shorter transmission frequency comes a concern that the range frommobile device to cell will be much shorter (e.g., hundreds of metersversus kilometers for 5G). While research has been conducted intosmaller cells and portable cells, there is little research into thecommunication requirements for temporal large groups, which may move ina dynamic way over a specified time period.

Existing methods fall short of enabling network cell management systemsto infer both bandwidth and crowd density within a location (e.g.,outdoor location). Embodiments of the present invention provide asolution to the problems associated with wireless network communicationwithin temporal large groups that move in a dynamic way over time. Inaspects, a system of the present invention provides a solution foroptimal location of mobile small cell infrastructure to ensure optimalquality of experience (QoE) (e.g., users can access data, data speedsare adequate for a user's needs) within a 5G network. In aspects, acomputer-implemented method is provided to: mine social user informationto understand the types of activity a user engages in at an event; minecrowd density/traffic pattern data to understand the movement ofindividuals within a crowd during the event; derive a social bandwidthdensity model (SBDM); infer QoE requirements within a geo-spatialboundary based on the SBDM; and deploy small mobile cell systems withina known trajectory based on the QoE requirements.

Advantageously, embodiments of the invention negate the costlyrequirement of having small cells at uniform distances within ageo-spatial boundary, regardless of whether all of the small cells arerequired or not. Moreover, embodiments of the invention enable adequateaccess and dissemination of content by mobile users at an event based oncrowdsourced data. Accordingly, embodiments of the invention provideimprovements in the technical field of wireless network communications.In aspects, unconventional advanced non-linear least squares (NLS)methods are utilized to infer optimal crowd density to address thetechnical problem of adequate cell coverage in a wireless network.Moreover, in aspects, historic crowd mapping/tracking of prior serviceusage from past like-themed events is utilized to determine technicalneeds of a wireless network associated with determined QoE data.Accordingly, methods of the invention utilizes new techniques foranalyzing data not previously utilized to generate new data to solvetechnical problems associated with wireless network usage events.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Referring now to FIG. 1, a schematic of an example of a computinginfrastructure is shown. Computing infrastructure 10 is only one exampleof a suitable computing infrastructure and is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe invention described herein. Regardless, computing infrastructure 10is capable of being implemented and/or performing any of thefunctionality set forth hereinabove.

In computing infrastructure 10 there is a computer system (or server)12, which is operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with computer system 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system 12 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 1, computer system 12 in computing infrastructure 10 isshown in the form of a general-purpose computing device. The componentsof computer system 12 may include, but are not limited to, one or moreprocessors or processing units (e.g., CPU) 16, a system memory 28, and abus 18 that couples various system components including system memory 28to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

FIG. 2 shows an exemplary wireless networking environment 50 inaccordance with aspects of the invention. The wireless networkingenvironment 50 includes a network 55 connecting a Quality of Experience(QoE) server 60 with one or more mobile devices 61. In aspects, thewireless networking environment 50 includes one or more master basestations 62 (e.g., stationary cell antenna) and one or more secondary ormobile base stations 63 (e.g., mobile cell antenna array). Inembodiments, the QoE server 60 is in communication with one or more userdevices 65, which may comprise hardware provisioning services.

In embodiments, the QoE server 60 comprises a computer system 12 of FIG.1, connected to the network 55 via the network adapter 20 of FIG. 1. Inaspects, the QoE server 60 is configured as a special purpose computingdevice that is part of a wireless network provider infrastructure. Forexample, the QoE server 60 may be configured to perform unconventionaldata analysis and analytics to derive social bandwidth density models(SBDMs) for use in inferring QoE bandwidth requirements for eventswithin specific geo-spatial boundaries.

The network 55 may be any suitable communication network or combinationof networks, such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet). Inembodiments, the network 55 is a cellular or mobile network distributedover a plurality of land areas (cells), each served by at least onefixed-location master base station 62 (e.g., base transceiver station orcellular radio tower). In aspects, one or more master base stations 62are configured to provide a cell(s) with network coverage which can beused for the transmission of voice, data and other types of content.Additionally, the one or more mobile base stations 63 (e.g., mobilemassive antenna arrays) provide a cell(s) with additional networkcoverage which can be used for the transmission of content. In aspects,the network 55 is configured to provide 5G network services to themobile devices 61. In embodiments, the master base stations 62 andmobile base stations 63 enable a plurality of portable transceivers(mobile devices 61) to communicate with fixed transceivers (e.g.,servers, desk top computers, etc.) and mobile devices 61 anywhere in thenetwork 55.

In aspects, the mobile devices 61 include one or more components of thecomputing device 12, and may be a laptop computer, tablet computer,smartphone, smart wearable device, automotive computing device, or otherportable computing device including wireless communication capabilities.In embodiments, one or more of the mobile devices 61 include acommunication module 64 enabling wireless communication via the network55. In aspects, the QoE server 60 is configured to communicate withplural mobile devices 61 simultaneously.

Still referring to FIG. 2, modules of the QoE server 60 are configuredto perform one or more of the functions described herein, and mayinclude one or more program modules (e.g., program module 42 of FIG. 1)executed by the QoE server 60. In embodiments, a data gathering module70 is configured to obtain social user data, crowd density and trafficpattern data, and/or other data relevant to the determination of networkusage and participant movement over time for an event. In aspects, theQoE server 60 gathers and stores usage data (e.g., participant dataregarding usage of network resources) in a usage database 74 of the QoEserver 60. In embodiments, an analytics module 76 of the QoE server 60is configured to determine bandwidth usage of the mobile devices 61,determine movement of participants (e.g., mobile devices 61) associatedwith an event, derive a social bandwidth density model (SBDM) frombandwidth and movement data, and infer QoE bandwidth requirements withina geo-spatial boundary associated with the event. In aspects, a planningmodule 78 is configured to generate and send notifications regardinggeographic deployment of one or more mobile base stations 63 tofacilitate the transfer of content among participants' mobile devices61, based on the inferred QoE bandwidth requirements.

In embodiments, the QoE server 60 includes additional or fewercomponents than those shown in FIG. 2. Separate components may beintegrated into a single computing component or module. Additionally, oralternatively, a single component may be implemented as multiplecomputing components or modules. Moreover, the type and quantity ofcomponents in the wireless networking environment 50 is not limited towhat is shown in FIG. 2. In practice, the wireless networkingenvironment 50 may include additional devices and/or networks; fewerdevices and/or networks; different devices and/or networks; ordifferently arranged devices and/or networks than illustrated in FIG. 2.

FIG. 3 shows a flowchart of a method in accordance with aspects of theinvention. Steps of the method of FIG. 3 may be performed in theenvironment illustrated in FIG. 2, and are described with reference toelements shown in FIG. 2.

At step 300, the QoE server 60 accesses and/or obtains social user data(crowd sourced data) for mobile device activities of a plurality ofusers (e.g., participants in an event of interest). The term crowdsourced as used herein refers to obtaining information by enlisting theservices of a large number of people through a network connection (e.g.,internet). In embodiments, step 300 is performed for users associatedwith a particular event of interest. An event of interest may be, forexample, a music concert, a festival, a political event, or othergathering of multiple participants where a wireless network is in use.In aspects, social user data accessed or obtained by the QoE server 60includes a variety of data enabling the QoE server 60 to understand thetypes of mobile device activities a user engages in at the event ofinterest via respective mobile devices 61. The types of activitiesengaged in by event participants results in a social footprint for eachindividual, enabling the QoE server 60 to understand the types ofwireless network services each individual consumes over time. A varietyof tools and methods may be utilized by the QoE server 60 to obtain thesocial user data. For example, the QoE server 60 may access a softwareapplication through an application programming interface (API) to obtaindata (e.g., posts per minute). In aspects, the QoE server 60 accessesread/write data of a mobile device 61 to understand activities (e.g.,uploaded files, streaming video, etc.) being performed by the mobiledevice 61. In embodiments, the data gathering module 70 of the QoEserver 60 performs step 300. In aspects, the QoE server 60 saves socialuser data for an event of interest in the usage database 74.

At step 301, the QoE server 60 determines bandwidth usage of mobiledevices 61 over time based on the social user data of step 300. Step 301may be performed in conjunction with step 300. In aspects, the QoEserver 60 saves bandwidth data for an event of interest in the usagedatabase 74. In embodiments, the data gathering module 70 of the QoEserver 60 determines the bandwidth according to step 301. In aspects,the data gathering module 70 of the QoE server 60 mines social userinformation of participants associated with the event of interest over aperiod of time to generate a table of results.

TABLE 1 Social Footprint of Participants Temporal Usage Bandwidth UserService (minutes) Used John Video Streaming 24 156 MB Paul SocialMessaging 45  34 MB George Picture Uploading 18  30 MB Jane SocialMessaging 36  28 MB

Table 1 represents social user information and bandwidth usage data forfour participants at an event over a fixed period of time. In accordancewith embodiments of the invention, the QoE server 60 obtains informationregarding mobile device activities of the users (e.g., mobile devices61), including video streaming, social messaging, and picture uploading.In the example of Table 1, a participant John spends a total of 24minutes during an event streaming digital video data on a mobile device,resulting in a total bandwidth usage of 156 megabytes (MB); aparticipant Paul spends a total of 45 minutes utilizing a socialmessaging tool of a mobile device, resulting in a total bandwidth usageof 34 MB; a participant George spends a total of 18 minutes uploadingdigital picture to a mobile device, resulting in a total bandwidth usageof 30 MB; and a participant Jane spends a total of 36 minutes utilizinga social messaging tool of a mobile device, resulting in a totalbandwidth usage of 28 MB.

At step 302, the QoE server 60 obtains crowd density data and trafficpattern data for an event of interest. In aspects, the QoE server 60saves crowd density data and traffic pattern data for an event ofinterest in the usage database 74. In aspects, the QoE server 60 obtainsglobal positioning system (GPS) data from a plurality of mobile devices61 associated with the event of interest (e.g., within predefinedgeographic area), and utilizes the GPS data to determine crowd densityand traffic patterns for the event of interest over time. Inembodiments, the data gathering module 70 of the QoE server 60 gatherscrowd density data and traffic pattern data in accordance with step 302.A variety of tools and methods may be utilized to determine crowddensity data and traffic pattern data over a period of time for an eventof interest.

In accordance with substep 302A, in embodiments, the QoE server 60utilizes a non-linear least squares (NLS) method to infer optimal crowddensity for an event. In general, if two or more variable are plottedvia a scatter plot, one can determine whether a line or a curve (orseries of curves) is the most appropriate method to fit a desired dataset. In aspects, the QoE server 60 employs a curve or series of curvefunctions that has/have the ability to pass as closely as possiblethrough plotted data points (e.g., plotted crowd density and trafficpattern data). The closer the line to the plotted points, the moreaccurate the estimated values of the line. A distance between anobserved value and an estimated value is known as a residual. Inaspects, the QoE server 60 is configured to produce the smallestcumulative value of residual errors. Many types of curve functionsexist. In aspects, the QoE server 60 utilizes one of the followingfunctions in the implementation of step 302A: quadratic, power,polynomial, rational, exponential, logarithmic or sinusoidal functions.

In accordance with substep 302B, in embodiments, the QoE server 60accesses historic crowd mapping and tracking data for an event which issimilar to the event of interest. In aspects, the QoE server 60 accessesdata of a prior event stored in the usage database 74. In embodiments,the QoE server 60 accesses historic crowd mapping and tracking data fora prior event, when the prior event is determined to match one or moreparameters of a current event of interest. In aspects, the QoE server 60determines a match between a current event and a historic event when athreshold is met (e.g., a predetermined number of matching parametersbetween the current and historic events is determined). In one example,the QoE server 60 determines that a type of event (e.g., music event)and a location (e.g., a music venue) from a prior event in the usagedatabase 74 matches the type of event and location of a current event ofinterest, and obtains crowd density and traffic pattern data for theprior event, wherein the prior event data is utilized to infer crowddensity and traffic patterns for the current event of interest.

At step 303, the QoE server 60 determines participant movement data(i.e., movement over time) based on the movement of individualparticipants at an event of interest and/or based on historic crowdmapping and tracking data (e.g., historic data from usage database 74).In aspects, the QoE server 60 generates a temporal plot illustratingcluster groups of users by their bandwidth usage (e.g., high, medium andlow usage) along an X/Y positioning system, based on the crowd densityand traffic pattern data of step 302. Recognizing that individual usersand cohorts of users will move in a pseudo random way over time, aspectsof the invention model this migration of users using Brownian motion.One example of a temporal plot generated in accordance with step 303 isillustrated at FIG. 4A, wherein clusters of high, medium and lowbandwidth usage are shown.

Referencing FIG. 3, at step 304, the QoE server 60 derives a socialbandwidth density model (SBDM) from the determined bandwidth datadetermined at step 301 and movement data determined at step 303. Inembodiments, the QoE server 60 uses a non-linear least squares model toplot a fitted curve, giving increased weight fitting to higher bandwidthrequirements. In the example of FIG. 4B, the plot of FIG. 4A is shownwith a Double Gaussian curve fit 400 to the movement and bandwidth (QoE)requirements of an event. An example of Double Gaussian fittedparameters is illustrated below (using the correlation coefficient (R)to compute the parameters).

Nonlinear Regression Model:y˜(a/b)*exp(−(x−c){circumflex over ( )}2/(2*b{circumflex over( )}2))+(d/e)*exp(−(x−f){circumflex over ( )}2/(2*e{circumflex over( )}2))Wherein: a=0.3989549; b=0.7500404; c=3.7499843; d=0.3989563; e=0.5000215and f=6.0000002. The residual sum-of-squares for this example is1.001.001 e-05.

At step 305 of FIG. 3, the QoE server 60 infers QoE bandwidthrequirements within a geo-spatial boundary using the SBDM model of step304. FIGS. 5A-5D illustrate how an SBDM model is used to infer QoErequirements. In particular, FIGS. 5A-5D illustrate the QoE server 60generating fitted curves 402 a-402 d, wherein the fitted curves arecomputed at regular intervals (illustrated by each of FIGS. 5A-5D) toallow for temporal movement and bandwidth changes during an event. Thatis, fitted curves 402 a-402 b differ from one another, as each curve isbased on different data as the data changes over the course of theevent. FIGS. 5A-5D illustrate the advantage of network cell positionsremaining mobile in order to allow for optimal distance betweenindividuals/cohorts and network cell stations at an event. Inembodiments, the QoE server 60 plots out optimal locations of one ormore network antennas (e.g., mobile base stations 63) using the SBDMmodel of step 304.

At step 306, the bandwidth requirements inferred at step 305 areutilized by the QoE server 60 to generate a notification. Inembodiments, the QoE server 60 generates a notification regardingdeployment of mobile cell systems within a known trajectory of theparticipants. In aspects, the QoE server 60 sends the notification ofstep 306 to one or more remote user devices 65. The notification mayinclude, for example, any message regarding the optimal or desirablelocations of one or more master base station (e.g., 5G base station), orone or more secondary base stations (e.g., portable 5G massive antennaarrays).

FIG. 6 illustrates optimal locations of network antennas A (e.g., mobilebase stations 63) along the fitted curve 400 of FIG. 4B based on theinferences of step 305 of FIG. 3. In this example, the QoE server 60generates the graph of FIG. 6, illustrating that an event of interestrequires four portable massive antenna arrays (e.g., mobile basestations 63) at different locations within a geo-spatial boundary, inorder to provide optimal QoE for network users at the event of interest.

FIG. 7 illustrates an exemplary use scenario wherein the QoE server 60determines optimal locations of mobile base stations based on networkusage information associated with an event of interest at a locationsindicated at 700. In this example, a geo-spatial boundary 700encompasses a large-scale event at a venue 702, including a parking lot703 and surrounding adjacent grounds represented at 704. Moreover, amaster base station 62 is shown, as well as a secondary or mobile basestation in the form of a massive antenna array 63A. In the example ofFIG. 7, the QoE server 60 (depicted in FIG. 2) obtains social user datafrom user mobile devices (e.g., wearable device 61A, car telematics 61B)at the large scale event. Social user data is obtained in accordancewith step 300 of FIG. 3. The QoE server 60 determines bandwidth usage ofmobile devices (e.g., wearable device 61A and car telematics 61B) withinthe geo-spatial boundary 700, obtains crowd density and traffic patterndata (e.g., GPS data from mobile devices within the geo-spatial boundary700), determines movement data, and derives a SBDM based on thebandwidth and movement data, in accordance with steps 301-304 of FIG. 3.In accordance with step 304 of FIG. 3, the QoE server 60 infers QoEbandwidth requirements for the geo-spatial boundary 700, and generates anotification indicating that four additional mobile antenna stationsrepresented at 63B-63E are needed in order to provide the functionalityand quality desired for the wireless network servicing the large scaleevent of interest.

In embodiments, a service provider could offer to perform the processesdescribed herein. In this case, the service provider can create,maintain, deploy, support, etc., the computer infrastructure thatperforms the process steps of the invention for one or more customers.These customers may be, for example, any business that uses technology.In return, the service provider can receive payment from the customer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still another embodiment, the invention provides acomputer-implemented method for determining hardware requirements for awireless network event using crowdsourcing. In this case, a computerinfrastructure, such as computer system 12 (FIG. 1), can be provided andone or more systems for performing the processes of the invention can beobtained (e.g., created, purchased, used, modified, etc.) and deployedto the computer infrastructure. To this extent, the deployment of asystem can comprise one or more of: (1) installing program code on acomputing device, such as computer system 12 (as shown in FIG. 1), froma computer-readable medium; (2) adding one or more computing devices tothe computer infrastructure; and (3) incorporating and/or modifying oneor more existing systems of the computer infrastructure to enable thecomputer infrastructure to perform the processes of the invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising:determining, by a computing device, participant movement for a pluralityof mobile devices associated with a current social event at a location,the participant movement data indicating the movement of participants atthe location of the current social event; deriving, by the computingdevice, a social bandwidth density model based on bandwidth usage datafor each of the plurality of mobile devices at the location during thecurrent social event and the participant movement data, wherein thebandwidth usage data is selected from the group consisting of: dataregarding bandwidth used for digital video streaming, data regardingbandwidth used for social messaging, data regarding bandwidth used fordigital picture uploading, and combinations thereof, and wherein thederiving the social bandwidth density model comprises plotting crowddensity data and traffic pattern data in a scatter plot; determining, bythe computing device, bandwidth requirements within a geo-spatialboundary associated with the social event from the social bandwidthdensity model; inferring, by the computing device, an optimal crowddensity for the current event utilizing a non-linear least squaresanalysis of the scatter plot to plot a fitted curve, wherein weightsutilized in plotting the fitted curve are based on bandwidthrequirements; and determining, by the computing device, that a historicevent is similar to the current social event based on matching athreshold number of parameters of the current event with parameters ofthe historic event, wherein historic crowd density data and historictraffic pattern data is derived from the historic event.
 2. Thecomputer-implemented method of claim 1, further comprising generating,by the computing device, a notification regarding deployment of one ormore mobile base stations at the current social event based on thedetermined bandwidth requirements.
 3. The computer-implemented method ofclaim 2, further comprising sending, by the computing device, thenotification to a remote computing device.
 4. The computer-implementedmethod of claim 1, further comprising receiving global positioningsystem data from the plurality of mobile devices.
 5. Thecomputer-implemented method of claim 1, wherein the participant movementdata is based on the historic crowd density data and the historictraffic pattern data.
 6. A computer program product comprising acomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computing device tocause the computing device to: generate participant movement data for aplurality of mobile devices associated with a current social event, theparticipant movement data indicating the movement of participants at alocation of the current social event; derive a social bandwidth densitymodel based on bandwidth usage data for each of the plurality of mobiledevices at the location during the current social event and theparticipant movement data, wherein the bandwidth usage data is selectedfrom the group consisting of: data regarding bandwidth used for digitalvideo streaming, data regarding bandwidth used for social messaging,data regarding bandwidth used for digital picture uploading, andcombinations thereof, and wherein the deriving the social bandwidthdensity model comprising plotting crowd density data and traffic patterndata in a scatter plot; determine bandwidth requirements within ageo-spatial boundary associated with the current social event from thesocial bandwidth density model; infer an optimal crowd density for thecurrent event utilizing a non-linear least squares analysis of thescatter plot to plot a fitted curve, wherein weights utilized inplotting the fitted curve are based on bandwidth requirements; anddetermine that a historic event is similar to the current social eventbased on matching a threshold number of parameters of the current eventwith parameters of the historic event, wherein historic crowd densitydata and historic traffic pattern data is derived from the historicevent.
 7. The computer program product of claim 6, wherein the programinstructions further cause the computing device to generate anotification regarding deployment of one or more mobile base stations atthe current social event based on the determined bandwidth requirements.8. The computer program product of claim 7, wherein the programinstructions further cause the computing device to send the notificationto a remote computing device.
 9. The computer program product of claim6, wherein the program instructions further cause the computing deviceto obtain crowd density data and traffic pattern data, and wherein theobtaining the crowd density data and traffic pattern data comprisesreceiving global positioning system data from the plurality of mobiledevices, and the participant movement data for the current social eventis based on the crowd density data and traffic pattern data.
 10. Thecomputer program product of claim 6, wherein the program instructionsfurther cause the computing device to obtain crowd density data andtraffic pattern data, and wherein the obtaining the crowd density dataand traffic pattern data comprises accessing the historic crowd densitydata and the historic traffic pattern data, and the participant movementdata for the social event is based on the historic crowd density dataand the historic traffic pattern data.
 11. A system comprising: aprocessor, a computer readable memory and a computer readable storagemedium associated with a computing device; program instructions todetermine participant movement data for a plurality of mobile devicesassociated with a current social event, the participant movement dataindicating the movement of participants at a location of the currentsocial event; program instructions to derive a social bandwidth densitymodel based on bandwidth usage data from the plurality of mobile devicesat the location of the current social event and the participant movementdata, wherein the bandwidth usage data is selected from the groupconsisting of: data regarding bandwidth used for digital videostreaming, data regarding bandwidth used for social messaging, dataregarding bandwidth used for digital picture uploading, and combinationsthereof, and wherein the deriving the social bandwidth density modelcomprises plotting crowd density data and traffic pattern data in ascatter plot; program instructions to determine bandwidth requirementswithin a geo-spatial boundary associated with the social event from thesocial bandwidth density model; program instructions to infer an optimalcrowd density for the current event utilizing a non-linear least squaresanalysis of the scatter plot to plot a fitted curve, wherein weightsutilized in plotting the fitted curve are based on bandwidthrequirements; and program instructions to determine that a historicevent is similar to the current social event based on matching athreshold number of parameters of the current event with parameters ofthe historic event, wherein historic crowd density data and historictraffic pattern data is derived from the historic event, wherein theprogram instructions are stored on the computer readable storage mediumfor execution by the processor via the computer readable memory.
 12. Thesystem of claim 11, further comprising: program instructions to generatea notification regarding deployment of one or more mobile base stationsat the current social event based on the determined bandwidthrequirements; and program instructions to send the notification to aremote computing device.
 13. The system of claim 11, wherein theparticipant movement data is based on global positioning system datafrom the plurality of mobile devices.
 14. The system of claim 11,wherein the participant movement data is based on the historic crowddensity data and the historic traffic pattern data.